基于動態(tài)樸素貝葉斯分類器的明渠水華風(fēng)險(xiǎn)評估模型
發(fā)布時(shí)間：2018-07-23 18:54
【摘要】：水華風(fēng)險(xiǎn)不僅是水利工程規(guī)劃時(shí)需要考慮的環(huán)境問題,也是水利設(shè)施運(yùn)營時(shí)不能忽視的監(jiān)測項(xiàng)目。為了提高明渠水化風(fēng)險(xiǎn)等級預(yù)測的準(zhǔn)確率,針對水華成因的不確定性和發(fā)展的時(shí)序性,基于動態(tài)樸素貝葉斯網(wǎng)絡(luò)分類器提出一種應(yīng)用于明渠的水華風(fēng)險(xiǎn)評估模型。模型用水華風(fēng)險(xiǎn)等級結(jié)點(diǎn)對應(yīng)藻葉綠素a(Chla)的濃度,并考慮了9項(xiàng)影響水藻生長的因素。采用主成分分析法,處理專家咨詢結(jié)果,進(jìn)行參數(shù)的設(shè)計(jì)。在蘇州河道北門橋2011年6月初至9月初觀測的53例連續(xù)監(jiān)測數(shù)據(jù)上,與基于樸素貝葉斯網(wǎng)絡(luò)分類器的評估模型進(jìn)行比較實(shí)驗(yàn)�；煜仃囷@示對中等風(fēng)險(xiǎn)情況的預(yù)測識別率提高了15.625%,單尾配對t檢驗(yàn)表明在顯著性水平0.05時(shí),兩模型預(yù)測識別率差異顯著�？紤]了時(shí)序特征的基于動態(tài)貝葉斯網(wǎng)絡(luò)分類器的評估模型對明渠中等水化風(fēng)險(xiǎn)的預(yù)測識別率提高顯著。
[Abstract]:Shui Hua risk is not only an environmental problem to be considered in the planning of water conservancy projects, but also a monitoring project which can not be ignored in the operation of water conservancy facilities. In order to improve the accuracy of prediction of open channel hydration risk class, a Shui Hua risk assessment model based on dynamic naive Bayesian network classifier is proposed for the uncertainty of Shui Hua cause and development time series. The water bloom risk level of the model corresponds to the concentration of chlorophyll a (Chla) of algae, and nine factors affecting the growth of algae were considered. The principal component analysis method was used to deal with the expert consultation results and the parameters were designed. In this paper, 53 consecutive monitoring data of Beimen Bridge in Suzhou River from the beginning of June to the beginning of September 2011 are compared with the evaluation model based on naive Bayesian network classifier. The confounding matrix showed that the prediction and recognition rate of medium risk cases was increased by 15.625%, and the single tail paired t test showed that there was a significant difference in prediction recognition rate between the two models at the significant level of 0.05. The evaluation model based on dynamic Bayesian network classifier with time series features is used to improve the prediction and recognition rate of open channel moderate hydration risk significantly.
【作者單位】： 河南工程學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(U1304702) 河南省科技廳軟科學(xué)項(xiàng)目(152400410480) 河南工程學(xué)院博士基金(D2015026)~~
【分類號】：TP18;TV672;X824
，
本文編號：2140338
[Abstract]:Shui Hua risk is not only an environmental problem to be considered in the planning of water conservancy projects, but also a monitoring project which can not be ignored in the operation of water conservancy facilities. In order to improve the accuracy of prediction of open channel hydration risk class, a Shui Hua risk assessment model based on dynamic naive Bayesian network classifier is proposed for the uncertainty of Shui Hua cause and development time series. The water bloom risk level of the model corresponds to the concentration of chlorophyll a (Chla) of algae, and nine factors affecting the growth of algae were considered. The principal component analysis method was used to deal with the expert consultation results and the parameters were designed. In this paper, 53 consecutive monitoring data of Beimen Bridge in Suzhou River from the beginning of June to the beginning of September 2011 are compared with the evaluation model based on naive Bayesian network classifier. The confounding matrix showed that the prediction and recognition rate of medium risk cases was increased by 15.625%, and the single tail paired t test showed that there was a significant difference in prediction recognition rate between the two models at the significant level of 0.05. The evaluation model based on dynamic Bayesian network classifier with time series features is used to improve the prediction and recognition rate of open channel moderate hydration risk significantly.
【作者單位】： 河南工程學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(U1304702) 河南省科技廳軟科學(xué)項(xiàng)目(152400410480) 河南工程學(xué)院博士基金(D2015026)~~
【分類號】：TP18;TV672;X824
，
本文編號：2140338
本文鏈接：http://sikaile.net/kejilunwen/shuiwenshuili/2140338.html
最近更新
教材專著
